Harper, S., Kaufman, J.S., Nandi A., Strumpf, E. Matching for Estimating Policy Effects. May 6, 2015.
Austin, P.C. (2011). An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate Behavioral Research, 46:399–424.
Gertler, P. J., Martinez, S., Premand, P., Rawlings, L. B., & Vermeersch, C. M. (2016). Chapter 8: Matching. Impact evaluation in practice. World Bank Publications.
Stuart, E.A. (2010). Matching Methods for Causal Inference: A Review and a Look Forward. Statistical Science Vol. 25, No. 1, 1–21.
Austin, P.C., & Stuart, E.A. (2015). Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Statistics in Medicine, Volume 34, Issue 28.
Brookhart, M.A., Schneeweiss, S., Rothman, K.J., Glynn, R.J., Avorn, J., Sturmer, T. (2006). Practice of Epidemiology: Variable Selection for Propensity Score Models. American Journal of Epidemiology, Vol. 163, No. 12.
DuGoff, E.H., Schuler, M., & Stuart, E.A. (2014). Generalizing Observational Study Results: Applying Propensity Score Methods to Complex Surveys. Health Services Research. Volume 49, Issue 1.
Lunt, M. (2013). Practice of Epidemiology: Selecting an Appropriate Caliper Can Be Essential for Achieving Good Balance With Propensity Score Matching. American Journal of Epidemiology, Volume 179, Issue 2.
Rudolph, K.E., Colson, K.E., Stuart, E.A., Ahern, J. (2016). Optimally combining propensity score subclasses. Statistics in Medicine, Volume 35, Issue 27.